Chen Rui, He Xiaolei
China Huaneng Group Co., Ltd., Beijing, China.
Jiangsu Huaneng Smart Energy Supply Chain Technology Co., Ltd., Nanjing, China.
PLoS One. 2025 Jul 3;20(7):e0326118. doi: 10.1371/journal.pone.0326118. eCollection 2025.
The foggy environment negatively affects car-following behavior, increasing rear-end collisions and energy consumption (including fuel consumption and traffic emissions). With advancements in technologies, connected automated vehicles (CAVs) are gradually replacing human-driven vehicles (HDVs) and becoming an integral part of transportation systems. The advent of CAVs offers a new approach to reducing car-following risks and energy consumption in foggy conditions. This study develops a fog-adaptive control framework for CAVs in foggy weather to mitigate car-following risks and reduce energy consumption. First, a foggy-weather car-following model, calibrated using driving simulator data, was selected to describe the behavior of HDVs in foggy highway conditions. Then, based on the model predictive control (MPC) theory, a CAV control strategy was proposed to minimize car-following risks and energy consumption in foggy weather. Finally, a simulation-based verification paradigm was established to assess objectives of risk reduction and energy saving under the proposed CAV strategy in mixed traffic. The results show that car-following risks and energy consumption vary under different fog densities and speed limit conditions. The proposed CAV control strategy can effectively reduce car-following risks by suppressing speed fluctuations, thereby lowering energy consumption in foggy mixed vehicular streams. At a 100% CAV penetration rate, the average reductions in various scenarios of fog density and speed limit conditions are as follows: ITC by 80.74%, DRAC by 59.44%, fuel consumption by 27.62%, CO2 emissions by 27.62%, CO emissions by 9.57%, HC emissions by 6.21%, and NOx emissions by 11.55%.
雾天环境会对跟车行为产生负面影响，增加追尾碰撞事故和能源消耗（包括燃油消耗和交通排放）。随着技术的进步，联网自动驾驶车辆（CAV）正逐渐取代人类驾驶车辆（HDV），并成为交通系统的重要组成部分。CAV的出现为降低雾天条件下的跟车风险和能源消耗提供了一种新方法。本研究针对雾天中的CAV开发了一种雾适应控制框架，以降低跟车风险并减少能源消耗。首先，选择一个通过驾驶模拟器数据校准的雾天跟车模型，来描述雾天高速公路条件下HDV的行为。然后，基于模型预测控制（MPC）理论，提出了一种CAV控制策略，以最小化雾天条件下的跟车风险和能源消耗。最后，建立了一个基于仿真的验证范式，以评估在混合交通中所提出的CAV策略下的风险降低和节能目标。结果表明，在不同的雾密度和速度限制条件下，跟车风险和能源消耗有所不同。所提出的CAV控制策略可以通过抑制速度波动来有效降低跟车风险，从而降低雾天混合车流中的能源消耗。在CAV渗透率为100%时，在各种雾密度和速度限制条件场景下的平均降低幅度如下：碰撞时间（ITC）降低80.74%，期望到达时间（DRAC）降低59.44%，燃油消耗降低27.62%，二氧化碳排放降低27.62%，一氧化碳排放降低9.57%，碳氢化合物排放降低6.21%，氮氧化物排放降低11.55%。
